Overview

Dataset statistics

Number of variables30
Number of observations341706
Missing cells180740
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory444.1 MiB
Average record size in memory1.3 KiB

Variable types

Text2
Categorical10
Numeric12
DateTime2
Unsupported4

Alerts

BANCOS_CREDITOS_ACTIVOS is highly overall correlated with BANCOS_CREDITOS_ACTIVOS_1High correlation
BANCOS_CREDITOS_ACTIVOS_1 is highly overall correlated with BANCOS_CREDITOS_ACTIVOSHigh correlation
Base is highly overall correlated with Motivo_Renuencia_Cliente and 4 other fieldsHigh correlation
CUOTAS_MERCADO_1 is highly overall correlated with SALDO_CASTIGO_DATACREDITO and 5 other fieldsHigh correlation
Edad__c is highly overall correlated with RANGO_EDAD and 1 other fieldsHigh correlation
Motivo_Renuencia_Cliente is highly overall correlated with Base and 4 other fieldsHigh correlation
Operado_Por__c is highly overall correlated with Base and 3 other fieldsHigh correlation
RANGO_EDAD is highly overall correlated with Edad__c and 1 other fieldsHigh correlation
RANGO_EDAD_1 is highly overall correlated with Edad__c and 1 other fieldsHigh correlation
RATIO_MORA_DEL_SALDO_TOTAL_REPORTADO is highly overall correlated with RATIO_MORA_DEL_SALDO_TOTAL_REPORTADO_1High correlation
RATIO_MORA_DEL_SALDO_TOTAL_REPORTADO_1 is highly overall correlated with RATIO_MORA_DEL_SALDO_TOTAL_REPORTADOHigh correlation
SALDO_CASTIGO_DATACREDITO is highly overall correlated with CUOTAS_MERCADO_1 and 5 other fieldsHigh correlation
SALDO_CASTIGO_DATACREDITO_1 is highly overall correlated with CUOTAS_MERCADO_1 and 5 other fieldsHigh correlation
SALDO_TOTAL_DATACREDITO is highly overall correlated with CUOTAS_MERCADO_1 and 5 other fieldsHigh correlation
SALDO_TOTAL_DATACREDITO_1 is highly overall correlated with CUOTAS_MERCADO_1 and 5 other fieldsHigh correlation
Saldo_Capital_cliente is highly overall correlated with CUOTAS_MERCADO_1 and 5 other fieldsHigh correlation
Saldo_Total_cliente is highly overall correlated with CUOTAS_MERCADO_1 and 5 other fieldsHigh correlation
Tipo_Cliente is highly overall correlated with Base and 4 other fieldsHigh correlation
estado is highly overall correlated with Base and 3 other fieldsHigh correlation
localizado_historico is highly overall correlated with Base and 2 other fieldsHigh correlation
RANGO_EDAD has 9533 (2.8%) missing valuesMissing
SALDO_TOTAL_DATACREDITO has 8327 (2.4%) missing valuesMissing
SALDO_CASTIGO_DATACREDITO has 8747 (2.6%) missing valuesMissing
BANCOS_CREDITOS_ACTIVOS has 13793 (4.0%) missing valuesMissing
RATIO_MORA_DEL_SALDO_TOTAL_REPORTADO has 8489 (2.5%) missing valuesMissing
RANGO_EDAD_1 has 3722 (1.1%) missing valuesMissing
CUOTAS_MERCADO_1 has 3538 (1.0%) missing valuesMissing
SALDO_TOTAL_DATACREDITO_1 has 15231 (4.5%) missing valuesMissing
SALDO_CASTIGO_DATACREDITO_1 has 53215 (15.6%) missing valuesMissing
RATIO_MORA_DEL_SALDO_TOTAL_REPORTADO_1 has 34599 (10.1%) missing valuesMissing
Contacto__c has unique valuesUnique
PUNTAJE is an unsupported type, check if it needs cleaning or further analysisUnsupported
INGRESOS_ESTIMADOS_DATACREDITO is an unsupported type, check if it needs cleaning or further analysisUnsupported
PUNTAJE_1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
INGRESOS_ESTIMADOS_DATACREDITO_1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
SALDO_CASTIGO_DATACREDITO has 7468 (2.2%) zerosZeros
RATIO_MORA_DEL_SALDO_TOTAL_REPORTADO has 3979 (1.2%) zerosZeros
CUOTAS_MERCADO_1 has 23662 (6.9%) zerosZeros
SALDO_TOTAL_DATACREDITO_1 has 6494 (1.9%) zerosZeros
BANCOS_CREDITOS_ACTIVOS_1 has 12318 (3.6%) zerosZeros
RATIO_MORA_DEL_SALDO_TOTAL_REPORTADO_1 has 6720 (2.0%) zerosZeros

Reproduction

Analysis started2025-07-07 04:05:16.550591
Analysis finished2025-07-07 04:08:34.330139
Duration3 minutes and 17.78 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Contacto__c
Text

UNIQUE 

Distinct341706
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size27.0 MiB
2025-07-06T23:08:34.816590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters6150708
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique341706 ?
Unique (%)100.0%

Sample

1st row0036e000043bxo7AAA
2nd row0035A00003XlKblQAF
3rd row0036e000046v5rCAAQ
4th row0035A00003XihQkQAJ
5th row0035A00003XihVTQAZ
ValueCountFrequency (%)
0036e000043bxo7aaa 1
 
< 0.1%
0035a00003xihvtqaz 1
 
< 0.1%
0035a00003xlkatqaf 1
 
< 0.1%
0035a00003xihsvqaz 1
 
< 0.1%
0035a00003xihvbqaz 1
 
< 0.1%
0035a00003xihv6qaj 1
 
< 0.1%
0035a00003xihwrqaz 1
 
< 0.1%
0035a00003xlkajqaf 1
 
< 0.1%
0035a00003xlkacqav 1
 
< 0.1%
0036e000046v5sdaaq 1
 
< 0.1%
Other values (341696) 341696
> 99.9%
2025-07-06T23:08:35.518381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2091737
34.0%
A 725245
 
11.8%
3 658445
 
10.7%
5 253870
 
4.1%
Q 197332
 
3.2%
6 191605
 
3.1%
e 191517
 
3.1%
4 125403
 
2.0%
B 112932
 
1.8%
X 102079
 
1.7%
Other values (52) 1500543
24.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3464784
56.3%
Uppercase Letter 1792045
29.1%
Lowercase Letter 893879
 
14.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 725245
40.5%
Q 197332
 
11.0%
B 112932
 
6.3%
X 102079
 
5.7%
C 56549
 
3.2%
Y 53666
 
3.0%
S 44653
 
2.5%
I 41319
 
2.3%
J 40947
 
2.3%
R 37226
 
2.1%
Other values (16) 380097
21.2%
Lowercase Letter
ValueCountFrequency (%)
e 191517
21.4%
k 80707
 
9.0%
b 75237
 
8.4%
n 55788
 
6.2%
r 42719
 
4.8%
u 39544
 
4.4%
l 39145
 
4.4%
t 31394
 
3.5%
x 30310
 
3.4%
c 24644
 
2.8%
Other values (16) 282874
31.6%
Decimal Number
ValueCountFrequency (%)
0 2091737
60.4%
3 658445
 
19.0%
5 253870
 
7.3%
6 191605
 
5.5%
4 125403
 
3.6%
9 53369
 
1.5%
2 32673
 
0.9%
1 25495
 
0.7%
8 17413
 
0.5%
7 14774
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 3464784
56.3%
Latin 2685924
43.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 725245
27.0%
Q 197332
 
7.3%
e 191517
 
7.1%
B 112932
 
4.2%
X 102079
 
3.8%
k 80707
 
3.0%
b 75237
 
2.8%
C 56549
 
2.1%
n 55788
 
2.1%
Y 53666
 
2.0%
Other values (42) 1034872
38.5%
Common
ValueCountFrequency (%)
0 2091737
60.4%
3 658445
 
19.0%
5 253870
 
7.3%
6 191605
 
5.5%
4 125403
 
3.6%
9 53369
 
1.5%
2 32673
 
0.9%
1 25495
 
0.7%
8 17413
 
0.5%
7 14774
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6150708
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2091737
34.0%
A 725245
 
11.8%
3 658445
 
10.7%
5 253870
 
4.1%
Q 197332
 
3.2%
6 191605
 
3.1%
e 191517
 
3.1%
4 125403
 
2.0%
B 112932
 
1.8%
X 102079
 
1.7%
Other values (52) 1500543
24.4%
Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.4 MiB
BANCO DE BOGOTA
70343 
BANCO DE BOGOTA5
44793 
BANCO DE BOGOTA3
36218 
BANCOLOMBIA2
34988 
TUYA2
28637 
Other values (15)
126727 

Length

Max length18
Median length17
Mean length12.837155
Min length4

Characters and Unicode

Total characters4386533
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBANCO DE BOGOTA4
2nd rowGRUPO SOL
3rd rowGRUPO SOL
4th rowGRUPO SOL
5th rowGRUPO SOL

Common Values

ValueCountFrequency (%)
BANCO DE BOGOTA 70343
20.6%
BANCO DE BOGOTA5 44793
13.1%
BANCO DE BOGOTA3 36218
10.6%
BANCOLOMBIA2 34988
10.2%
TUYA2 28637
8.4%
BANCO DE BOGOTA2 24782
 
7.3%
BANCOLOMBIA 22373
 
6.5%
TUYA 15454
 
4.5%
BANCO DE OCCIDENTE 14276
 
4.2%
BANCAMIA 12334
 
3.6%
Other values (10) 37508
11.0%

Length

2025-07-06T23:08:35.786504image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
banco 198138
26.2%
de 198138
26.2%
bogota 70343
 
9.3%
bogota5 44793
 
5.9%
bogota3 36218
 
4.8%
bancolombia2 34988
 
4.6%
tuya2 28637
 
3.8%
bogota2 24782
 
3.3%
bancolombia 22373
 
3.0%
tuya 15454
 
2.0%
Other values (16) 82005
10.8%

Most occurring characters

ValueCountFrequency (%)
O 714832
16.3%
A 627303
14.3%
B 509056
11.6%
414163
9.4%
C 307084
7.0%
N 300798
6.9%
T 260663
 
5.9%
E 228901
 
5.2%
D 215624
 
4.9%
G 192713
 
4.4%
Other values (15) 615396
14.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3792995
86.5%
Space Separator 414163
 
9.4%
Decimal Number 179375
 
4.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 714832
18.8%
A 627303
16.5%
B 509056
13.4%
C 307084
8.1%
N 300798
7.9%
T 260663
 
6.9%
E 228901
 
6.0%
D 215624
 
5.7%
G 192713
 
5.1%
I 122379
 
3.2%
Other values (10) 313642
8.3%
Decimal Number
ValueCountFrequency (%)
2 90638
50.5%
5 44793
25.0%
3 36218
 
20.2%
4 7726
 
4.3%
Space Separator
ValueCountFrequency (%)
414163
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3792995
86.5%
Common 593538
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 714832
18.8%
A 627303
16.5%
B 509056
13.4%
C 307084
8.1%
N 300798
7.9%
T 260663
 
6.9%
E 228901
 
6.0%
D 215624
 
5.7%
G 192713
 
5.1%
I 122379
 
3.2%
Other values (10) 313642
8.3%
Common
ValueCountFrequency (%)
414163
69.8%
2 90638
 
15.3%
5 44793
 
7.5%
3 36218
 
6.1%
4 7726
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4386533
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 714832
16.3%
A 627303
14.3%
B 509056
11.6%
414163
9.4%
C 307084
7.0%
N 300798
6.9%
T 260663
 
5.9%
E 228901
 
5.2%
D 215624
 
4.9%
G 192713
 
4.4%
Other values (15) 615396
14.0%

Operado_Por__c
Categorical

HIGH CORRELATION 

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.1 MiB
DIGITAL
102600 
PAZ_SALVO
49062 
QNT_RBK_1
27275 
QNT_BB3
20521 
QNT_OS_MONTOSBAJOS
16947 
Other values (35)
125301 

Length

Max length34
Median length33
Mean length8.8938064
Min length3

Characters and Unicode

Total characters3039067
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAYS_BB4
2nd rowDIGITAL
3rd rowDIGITAL
4th rowDIGITAL
5th rowDIGITAL

Common Values

ValueCountFrequency (%)
DIGITAL 102600
30.0%
PAZ_SALVO 49062
14.4%
QNT_RBK_1 27275
 
8.0%
QNT_BB3 20521
 
6.0%
QNT_OS_MONTOSBAJOS 16947
 
5.0%
QNT_RBK_2 15969
 
4.7%
CX_RBK 8931
 
2.6%
QNT_OCC 7742
 
2.3%
NEXTDATA_BB5_JUD 7508
 
2.2%
CX_BB5_JUD 7227
 
2.1%
Other values (30) 77924
22.8%

Length

2025-07-06T23:08:36.033172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
digital 102600
30.0%
paz_salvo 49062
14.4%
qnt_rbk_1 27275
 
8.0%
qnt_bb3 20521
 
6.0%
qnt_os_montosbajos 16947
 
5.0%
qnt_rbk_2 15969
 
4.7%
cx_rbk 8931
 
2.6%
qnt_occ 7742
 
2.3%
nextdata_bb5_jud 7508
 
2.2%
cx_bb5_jud 7227
 
2.1%
Other values (30) 77924
22.8%

Most occurring characters

ValueCountFrequency (%)
A 306088
 
10.1%
_ 293568
 
9.7%
T 281806
 
9.3%
I 230962
 
7.6%
B 196421
 
6.5%
O 178144
 
5.9%
N 176341
 
5.8%
D 164064
 
5.4%
L 158534
 
5.2%
S 131533
 
4.3%
Other values (22) 921606
30.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2621717
86.3%
Connector Punctuation 293568
 
9.7%
Decimal Number 103364
 
3.4%
Lowercase Letter 10211
 
0.3%
Dash Punctuation 10207
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 306088
11.7%
T 281806
10.7%
I 230962
 
8.8%
B 196421
 
7.5%
O 178144
 
6.8%
N 176341
 
6.7%
D 164064
 
6.3%
L 158534
 
6.0%
S 131533
 
5.0%
G 106498
 
4.1%
Other values (14) 691326
26.4%
Decimal Number
ValueCountFrequency (%)
5 29922
28.9%
1 27275
26.4%
2 20909
20.2%
3 20521
19.9%
4 4737
 
4.6%
Connector Punctuation
ValueCountFrequency (%)
_ 293568
100.0%
Lowercase Letter
ValueCountFrequency (%)
y 10211
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10207
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2631928
86.6%
Common 407139
 
13.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 306088
11.6%
T 281806
10.7%
I 230962
 
8.8%
B 196421
 
7.5%
O 178144
 
6.8%
N 176341
 
6.7%
D 164064
 
6.2%
L 158534
 
6.0%
S 131533
 
5.0%
G 106498
 
4.0%
Other values (15) 701537
26.7%
Common
ValueCountFrequency (%)
_ 293568
72.1%
5 29922
 
7.3%
1 27275
 
6.7%
2 20909
 
5.1%
3 20521
 
5.0%
- 10207
 
2.5%
4 4737
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3039067
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 306088
 
10.1%
_ 293568
 
9.7%
T 281806
 
9.3%
I 230962
 
7.6%
B 196421
 
6.5%
O 178144
 
5.9%
N 176341
 
5.8%
D 164064
 
5.4%
L 158534
 
5.2%
S 131533
 
4.3%
Other values (22) 921606
30.3%

Saldo_Capital_cliente
Real number (ℝ)

HIGH CORRELATION 

Distinct326011
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6137716.5
Minimum0
Maximum6.6418115 × 108
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2025-07-06T23:08:36.283086image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile492551.25
Q11189304.2
median3014919.5
Q36866761.5
95-th percentile22541596
Maximum6.6418115 × 108
Range6.6418115 × 108
Interquartile range (IQR)5677457.2

Descriptive statistics

Standard deviation9962015.4
Coefficient of variation (CV)1.6230817
Kurtosis174.96326
Mean6137716.5
Median Absolute Deviation (MAD)2113175.5
Skewness7.3004448
Sum2.0972946 × 1012
Variance9.9241751 × 1013
MonotonicityNot monotonic
2025-07-06T23:08:36.537606image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120000 385
 
0.1%
500000 378
 
0.1%
1242090 363
 
0.1%
700000 287
 
0.1%
1242174 281
 
0.1%
600000 252
 
0.1%
300000 245
 
0.1%
1000000 240
 
0.1%
800000 195
 
0.1%
110794 195
 
0.1%
Other values (326001) 338885
99.2%
ValueCountFrequency (%)
0 3
< 0.1%
1 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
21 1
 
< 0.1%
142 1
 
< 0.1%
1184 1
 
< 0.1%
1223 1
 
< 0.1%
1550 1
 
< 0.1%
1574 1
 
< 0.1%
ValueCountFrequency (%)
664181151 1
< 0.1%
591158586 1
< 0.1%
538210981 1
< 0.1%
364155789 1
< 0.1%
321284333 1
< 0.1%
270142033 1
< 0.1%
269070589 1
< 0.1%
253775674 1
< 0.1%
253743212 1
< 0.1%
253594853 1
< 0.1%

Saldo_Total_cliente
Real number (ℝ)

HIGH CORRELATION 

Distinct336919
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18954263
Minimum0
Maximum6.0148234 × 109
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2025-07-06T23:08:36.818848image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile829167.25
Q12179316.8
median5266021
Q313541606
95-th percentile86063904
Maximum6.0148234 × 109
Range6.0148234 × 109
Interquartile range (IQR)11362290

Descriptive statistics

Standard deviation51381643
Coefficient of variation (CV)2.7108226
Kurtosis927.19485
Mean18954263
Median Absolute Deviation (MAD)3743200
Skewness15.766182
Sum6.4767852 × 1012
Variance2.6400732 × 1015
MonotonicityNot monotonic
2025-07-06T23:08:37.104028image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
646522 59
 
< 0.1%
608572 21
 
< 0.1%
598222 19
 
< 0.1%
151690 18
 
< 0.1%
441450 14
 
< 0.1%
862029 12
 
< 0.1%
592644 11
 
< 0.1%
152345 10
 
< 0.1%
910956 10
 
< 0.1%
587872 9
 
< 0.1%
Other values (336909) 341523
99.9%
ValueCountFrequency (%)
0 3
< 0.1%
2 1
 
< 0.1%
10 1
 
< 0.1%
17 1
 
< 0.1%
24 1
 
< 0.1%
161 1
 
< 0.1%
1415 1
 
< 0.1%
1603 1
 
< 0.1%
1773 1
 
< 0.1%
1871 1
 
< 0.1%
ValueCountFrequency (%)
6014823401 1
< 0.1%
4245160770 1
< 0.1%
4000141911 1
< 0.1%
2382531777 1
< 0.1%
2313937771 1
< 0.1%
2038192840 1
< 0.1%
2032587733 1
< 0.1%
2013072612 1
< 0.1%
1762674417 1
< 0.1%
1751234918 1
< 0.1%

Edad__c
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)< 0.1%
Missing23
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean42.194815
Minimum0
Maximum158
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2025-07-06T23:08:37.347212image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q135
median41
Q351
95-th percentile61
Maximum158
Range158
Interquartile range (IQR)16

Descriptive statistics

Standard deviation14.789317
Coefficient of variation (CV)0.35050082
Kurtosis8.2916736
Mean42.194815
Median Absolute Deviation (MAD)7
Skewness1.3485555
Sum14417251
Variance218.7239
MonotonicityNot monotonic
2025-07-06T23:08:37.879250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44 34939
 
10.2%
37 31870
 
9.3%
41 27775
 
8.1%
42 24934
 
7.3%
51 20656
 
6.0%
54 20624
 
6.0%
35 20064
 
5.9%
34 19682
 
5.8%
61 19546
 
5.7%
29 17944
 
5.3%
Other values (73) 103649
30.3%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 1549
 
0.5%
2 6635
1.9%
3 157
 
< 0.1%
7 1
 
< 0.1%
11 1
 
< 0.1%
18 1
 
< 0.1%
20 1
 
< 0.1%
21 109
 
< 0.1%
22 161
 
< 0.1%
ValueCountFrequency (%)
158 1
 
< 0.1%
154 1
 
< 0.1%
153 1
 
< 0.1%
148 1
 
< 0.1%
135 1
 
< 0.1%
123 1
 
< 0.1%
122 3713
1.1%
90 1
 
< 0.1%
88 1
 
< 0.1%
86 1
 
< 0.1%

Genero__c
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.5 MiB
M
195676 
F
146030 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters341706
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 195676
57.3%
F 146030
42.7%

Length

2025-07-06T23:08:38.115771image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T23:08:38.334102image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
m 195676
57.3%
f 146030
42.7%

Most occurring characters

ValueCountFrequency (%)
M 195676
57.3%
F 146030
42.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 341706
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 195676
57.3%
F 146030
42.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 341706
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 195676
57.3%
F 146030
42.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 341706
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 195676
57.3%
F 146030
42.7%

Ciudad
Text

Distinct949
Distinct (%)0.3%
Missing128
Missing (%)< 0.1%
Memory size24.3 MiB
2025-07-06T23:08:38.646953image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length27
Median length23
Mean length9.5071814
Min length4

Characters and Unicode

Total characters3247444
Distinct characters30
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)< 0.1%

Sample

1st rowSIN INFORMACION
2nd rowSIN INFORMACION
3rd rowSIN INFORMACION
4th rowSIN INFORMACION
5th rowSIN INFORMACION
ValueCountFrequency (%)
bogota 101400
20.3%
d.c 101400
20.3%
sin 32507
 
6.5%
informacion 32507
 
6.5%
cali 32352
 
6.5%
medellin 15141
 
3.0%
barranquilla 12937
 
2.6%
bucaramanga 7875
 
1.6%
cartagena 7415
 
1.5%
cucuta 5592
 
1.1%
Other values (937) 149663
30.0%
2025-07-06T23:08:39.245266image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 466259
14.4%
O 339866
 
10.5%
I 245076
 
7.5%
C 243705
 
7.5%
. 202800
 
6.2%
N 202372
 
6.2%
157211
 
4.8%
T 155345
 
4.8%
L 152850
 
4.7%
B 150893
 
4.6%
Other values (20) 931067
28.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2886579
88.9%
Other Punctuation 202800
 
6.2%
Space Separator 157211
 
4.8%
Initial Punctuation 828
 
< 0.1%
Lowercase Letter 19
 
< 0.1%
Control 7
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 466259
16.2%
O 339866
11.8%
I 245076
 
8.5%
C 243705
 
8.4%
N 202372
 
7.0%
T 155345
 
5.4%
L 152850
 
5.3%
B 150893
 
5.2%
D 150555
 
5.2%
G 141928
 
4.9%
Other values (15) 637730
22.1%
Other Punctuation
ValueCountFrequency (%)
. 202800
100.0%
Space Separator
ValueCountFrequency (%)
157211
100.0%
Initial Punctuation
ValueCountFrequency (%)
828
100.0%
Lowercase Letter
ValueCountFrequency (%)
s 19
100.0%
Control
ValueCountFrequency (%)
 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2886598
88.9%
Common 360846
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 466259
16.2%
O 339866
11.8%
I 245076
 
8.5%
C 243705
 
8.4%
N 202372
 
7.0%
T 155345
 
5.4%
L 152850
 
5.3%
B 150893
 
5.2%
D 150555
 
5.2%
G 141928
 
4.9%
Other values (16) 637749
22.1%
Common
ValueCountFrequency (%)
. 202800
56.2%
157211
43.6%
828
 
0.2%
 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3245774
99.9%
None 842
 
< 0.1%
Punctuation 828
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 466259
14.4%
O 339866
 
10.5%
I 245076
 
7.6%
C 243705
 
7.5%
. 202800
 
6.2%
N 202372
 
6.2%
157211
 
4.8%
T 155345
 
4.8%
L 152850
 
4.7%
B 150893
 
4.6%
Other values (17) 929397
28.6%
None
ValueCountFrequency (%)
à 835
99.2%
 7
 
0.8%
Punctuation
ValueCountFrequency (%)
828
100.0%

estado
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing512
Missing (%)0.1%
Memory size25.4 MiB
POR_CONTACTAR
255647 
MANTENIMIENTO
65219 
POR_ACORDAR
 
19213
POR_PAGAR
 
1115

Length

Max length13
Median length13
Mean length12.874306
Min length9

Characters and Unicode

Total characters4392636
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPOR_ACORDAR
2nd rowPOR_ACORDAR
3rd rowMANTENIMIENTO
4th rowMANTENIMIENTO
5th rowMANTENIMIENTO

Common Values

ValueCountFrequency (%)
POR_CONTACTAR 255647
74.8%
MANTENIMIENTO 65219
 
19.1%
POR_ACORDAR 19213
 
5.6%
POR_PAGAR 1115
 
0.3%
(Missing) 512
 
0.1%

Length

2025-07-06T23:08:39.535619image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T23:08:39.775992image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
por_contactar 255647
74.9%
mantenimiento 65219
 
19.1%
por_acordar 19213
 
5.6%
por_pagar 1115
 
0.3%

Most occurring characters

ValueCountFrequency (%)
T 641732
14.6%
A 617169
14.1%
O 616054
14.0%
R 571163
13.0%
C 530507
12.1%
N 451304
10.3%
P 277090
6.3%
_ 275975
6.3%
M 130438
 
3.0%
E 130438
 
3.0%
Other values (3) 150766
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4116661
93.7%
Connector Punctuation 275975
 
6.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 641732
15.6%
A 617169
15.0%
O 616054
15.0%
R 571163
13.9%
C 530507
12.9%
N 451304
11.0%
P 277090
6.7%
M 130438
 
3.2%
E 130438
 
3.2%
I 130438
 
3.2%
Other values (2) 20328
 
0.5%
Connector Punctuation
ValueCountFrequency (%)
_ 275975
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4116661
93.7%
Common 275975
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 641732
15.6%
A 617169
15.0%
O 616054
15.0%
R 571163
13.9%
C 530507
12.9%
N 451304
11.0%
P 277090
6.7%
M 130438
 
3.2%
E 130438
 
3.2%
I 130438
 
3.2%
Other values (2) 20328
 
0.5%
Common
ValueCountFrequency (%)
_ 275975
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4392636
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 641732
14.6%
A 617169
14.1%
O 616054
14.0%
R 571163
13.0%
C 530507
12.1%
N 451304
10.3%
P 277090
6.3%
_ 275975
6.3%
M 130438
 
3.0%
E 130438
 
3.0%
Other values (3) 150766
 
3.4%

localizado_historico
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing516
Missing (%)0.2%
Memory size25.1 MiB
NO_LOCALIZADO
224147 
LOCALIZADO
117043 

Length

Max length13
Median length13
Mean length11.97087
Min length10

Characters and Unicode

Total characters4084341
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLOCALIZADO
2nd rowLOCALIZADO
3rd rowLOCALIZADO
4th rowLOCALIZADO
5th rowLOCALIZADO

Common Values

ValueCountFrequency (%)
NO_LOCALIZADO 224147
65.6%
LOCALIZADO 117043
34.3%
(Missing) 516
 
0.2%

Length

2025-07-06T23:08:40.036149image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T23:08:40.307183image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
no_localizado 224147
65.7%
localizado 117043
34.3%

Most occurring characters

ValueCountFrequency (%)
O 906527
22.2%
L 682380
16.7%
A 682380
16.7%
C 341190
 
8.4%
I 341190
 
8.4%
Z 341190
 
8.4%
D 341190
 
8.4%
N 224147
 
5.5%
_ 224147
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3860194
94.5%
Connector Punctuation 224147
 
5.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 906527
23.5%
L 682380
17.7%
A 682380
17.7%
C 341190
 
8.8%
I 341190
 
8.8%
Z 341190
 
8.8%
D 341190
 
8.8%
N 224147
 
5.8%
Connector Punctuation
ValueCountFrequency (%)
_ 224147
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3860194
94.5%
Common 224147
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 906527
23.5%
L 682380
17.7%
A 682380
17.7%
C 341190
 
8.8%
I 341190
 
8.8%
Z 341190
 
8.8%
D 341190
 
8.8%
N 224147
 
5.8%
Common
ValueCountFrequency (%)
_ 224147
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4084341
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 906527
22.2%
L 682380
16.7%
A 682380
16.7%
C 341190
 
8.4%
I 341190
 
8.4%
Z 341190
 
8.4%
D 341190
 
8.4%
N 224147
 
5.5%
_ 224147
 
5.5%

Motivo_Renuencia_Cliente
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)< 0.1%
Missing368
Missing (%)0.1%
Memory size32.8 MiB
CLIENTE SIN NINGUN CONTACTO HISTORICO
167826 
CLIENTE CANCELADO
51022 
CLIENTE PERDIDO
28182 
CLIENTE NO PERDIDO QUIEN NO ACEPTA POR DIVERSOS MOTIVOS
25968 
CLIENTE NO PERDIDO QUIEN CUELGA ANTE EL OFRECIMIENTO
21906 
Other values (4)
46434 

Length

Max length56
Median length55
Mean length35.643503
Min length15

Characters and Unicode

Total characters12166482
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCLIENTE NO PERDIDO QUIEN NO ACEPTA POR DIVERSOS MOTIVOS
2nd rowCLIENTE PERDIDO
3rd rowCLIENTE PERDIDO
4th rowCLIENTE PERDIDO
5th rowCLIENTE PERDIDO

Common Values

ValueCountFrequency (%)
CLIENTE SIN NINGUN CONTACTO HISTORICO 167826
49.1%
CLIENTE CANCELADO 51022
 
14.9%
CLIENTE PERDIDO 28182
 
8.2%
CLIENTE NO PERDIDO QUIEN NO ACEPTA POR DIVERSOS MOTIVOS 25968
 
7.6%
CLIENTE NO PERDIDO QUIEN CUELGA ANTE EL OFRECIMIENTO 21906
 
6.4%
CLIENTE NO PERDIDO QUIEN NO ACEPTA POR CAPACIDAD DE PAGO 14315
 
4.2%
CLIENTE CON ALGUN PAGO HISTORICO 13951
 
4.1%
CLIENTE NO PERDIDO QUIEN NO ACEPTA POR EMPLEO 13939
 
4.1%
CLIENTE NO PERDIDO QUIEN REITERA EXCUSAS DE TODO TIPO 4229
 
1.2%
(Missing) 368
 
0.1%

Length

2025-07-06T23:08:40.543846image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T23:08:40.812344image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
cliente 341338
19.3%
historico 181777
10.3%
ningun 167826
9.5%
contacto 167826
9.5%
sin 167826
9.5%
no 134579
 
7.6%
perdido 108539
 
6.1%
quien 80357
 
4.5%
acepta 54222
 
3.1%
por 54222
 
3.1%
Other values (17) 310464
17.6%

Most occurring characters

ValueCountFrequency (%)
N 1518140
12.5%
1427638
11.7%
I 1347961
11.1%
O 1238127
10.2%
E 1171423
9.6%
C 1105655
9.1%
T 995456
8.2%
A 515746
 
4.2%
L 464062
 
3.8%
S 435965
 
3.6%
Other values (11) 1946309
16.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10738844
88.3%
Space Separator 1427638
 
11.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 1518140
14.1%
I 1347961
12.6%
O 1238127
11.5%
E 1171423
10.9%
C 1105655
10.3%
T 995456
9.3%
A 515746
 
4.8%
L 464062
 
4.3%
S 435965
 
4.1%
R 400870
 
3.7%
Other values (10) 1545439
14.4%
Space Separator
ValueCountFrequency (%)
1427638
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10738844
88.3%
Common 1427638
 
11.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 1518140
14.1%
I 1347961
12.6%
O 1238127
11.5%
E 1171423
10.9%
C 1105655
10.3%
T 995456
9.3%
A 515746
 
4.8%
L 464062
 
4.3%
S 435965
 
4.1%
R 400870
 
3.7%
Other values (10) 1545439
14.4%
Common
ValueCountFrequency (%)
1427638
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12166482
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 1518140
12.5%
1427638
11.7%
I 1347961
11.1%
O 1238127
10.2%
E 1171423
9.6%
C 1105655
9.1%
T 995456
8.2%
A 515746
 
4.2%
L 464062
 
3.8%
S 435965
 
3.6%
Other values (11) 1946309
16.0%

RANGO_EDAD
Categorical

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)< 0.1%
Missing9533
Missing (%)2.8%
Memory size22.6 MiB
36-45
99419 
29-35
84050 
46-55
63976 
22-28
52035 
56-65
22616 
Other values (4)
10077 

Length

Max length5
Median length5
Mean length4.9572994
Min length3

Characters and Unicode

Total characters1646681
Distinct characters11
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row29-35
2nd row36-45
3rd row56-65
4th row66+
5th row56-65

Common Values

ValueCountFrequency (%)
36-45 99419
29.1%
29-35 84050
24.6%
46-55 63976
18.7%
22-28 52035
15.2%
56-65 22616
 
6.6%
66+ 7092
 
2.1%
18-21 2648
 
0.8%
66+ 329
 
0.1%
8
 
< 0.1%
(Missing) 9533
 
2.8%

Length

2025-07-06T23:08:41.092652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T23:08:41.349564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
36-45 99419
29.9%
29-35 84050
25.3%
46-55 63976
19.3%
22-28 52035
15.7%
56-65 22616
 
6.8%
66 7421
 
2.2%
18-21 2648
 
0.8%

Most occurring characters

ValueCountFrequency (%)
5 356653
21.7%
- 324744
19.7%
2 242803
14.7%
6 223469
13.6%
3 183469
11.1%
4 163395
9.9%
9 84050
 
5.1%
8 54683
 
3.3%
+ 7421
 
0.5%
1 5296
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1313818
79.8%
Dash Punctuation 324744
 
19.7%
Math Symbol 7421
 
0.5%
Space Separator 698
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 356653
27.1%
2 242803
18.5%
6 223469
17.0%
3 183469
14.0%
4 163395
12.4%
9 84050
 
6.4%
8 54683
 
4.2%
1 5296
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 324744
100.0%
Math Symbol
ValueCountFrequency (%)
+ 7421
100.0%
Space Separator
ValueCountFrequency (%)
698
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1646681
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 356653
21.7%
- 324744
19.7%
2 242803
14.7%
6 223469
13.6%
3 183469
11.1%
4 163395
9.9%
9 84050
 
5.1%
8 54683
 
3.3%
+ 7421
 
0.5%
1 5296
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1646681
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 356653
21.7%
- 324744
19.7%
2 242803
14.7%
6 223469
13.6%
3 183469
11.1%
4 163395
9.9%
9 84050
 
5.1%
8 54683
 
3.3%
+ 7421
 
0.5%
1 5296
 
0.3%
Distinct85
Distinct (%)< 0.1%
Missing2664
Missing (%)0.8%
Memory size5.2 MiB
Minimum2018-10-23 00:00:00+00:00
Maximum2022-07-22 00:00:00+00:00
2025-07-06T23:08:41.622205image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:41.907346image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PUNTAJE
Unsupported

REJECTED  UNSUPPORTED 

Missing2786
Missing (%)0.8%
Memory size24.0 MiB

INGRESOS_ESTIMADOS_DATACREDITO
Unsupported

REJECTED  UNSUPPORTED 

Missing2795
Missing (%)0.8%
Memory size23.9 MiB

SALDO_TOTAL_DATACREDITO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct81476
Distinct (%)24.4%
Missing8327
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean26293.937
Minimum0
Maximum5249551
Zeros1864
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2025-07-06T23:08:42.158906image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1079
Q13960
median11103
Q328090.5
95-th percentile96498.5
Maximum5249551
Range5249551
Interquartile range (IQR)24130.5

Descriptive statistics

Standard deviation55241.755
Coefficient of variation (CV)2.1009313
Kurtosis579.3076
Mean26293.937
Median Absolute Deviation (MAD)8596
Skewness14.244172
Sum8.7658464 × 109
Variance3.0516515 × 109
MonotonicityNot monotonic
2025-07-06T23:08:42.407540image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1864
 
0.5%
1878 45
 
< 0.1%
1808 44
 
< 0.1%
124 44
 
< 0.1%
1629 42
 
< 0.1%
1807 42
 
< 0.1%
2145 42
 
< 0.1%
1665 41
 
< 0.1%
2168 41
 
< 0.1%
1679 40
 
< 0.1%
Other values (81466) 331134
96.9%
(Missing) 8327
 
2.4%
ValueCountFrequency (%)
0 1864
0.5%
1 3
 
< 0.1%
2 2
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 4
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
5249551 1
< 0.1%
4122696 1
< 0.1%
2807356 1
< 0.1%
2741651 1
< 0.1%
2645058 1
< 0.1%
2578617 1
< 0.1%
2538955 1
< 0.1%
2372671 1
< 0.1%
2084091 1
< 0.1%
2041891 1
< 0.1%

SALDO_CASTIGO_DATACREDITO
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct68216
Distinct (%)20.5%
Missing8747
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean19126.325
Minimum0
Maximum4088234
Zeros7468
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2025-07-06T23:08:42.661065image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile624
Q12622
median7753
Q319954.5
95-th percentile72533.1
Maximum4088234
Range4088234
Interquartile range (IQR)17332.5

Descriptive statistics

Standard deviation40624.973
Coefficient of variation (CV)2.1240344
Kurtosis535.04942
Mean19126.325
Median Absolute Deviation (MAD)6104
Skewness13.160798
Sum6.3682822 × 109
Variance1.6503884 × 109
MonotonicityNot monotonic
2025-07-06T23:08:42.942448image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7468
 
2.2%
124 126
 
< 0.1%
123 90
 
< 0.1%
125 87
 
< 0.1%
700 80
 
< 0.1%
500 73
 
< 0.1%
600 61
 
< 0.1%
599 61
 
< 0.1%
800 60
 
< 0.1%
1000 59
 
< 0.1%
Other values (68206) 324794
95.1%
(Missing) 8747
 
2.6%
ValueCountFrequency (%)
0 7468
2.2%
2 3
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
13 2
 
< 0.1%
14 3
 
< 0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
ValueCountFrequency (%)
4088234 1
< 0.1%
1984343 1
< 0.1%
1950637 1
< 0.1%
1947521 1
< 0.1%
1856787 1
< 0.1%
1788418 1
< 0.1%
1762018 1
< 0.1%
1580489 1
< 0.1%
1576184 1
< 0.1%
1534371 1
< 0.1%

BANCOS_CREDITOS_ACTIVOS
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)< 0.1%
Missing13793
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean2.7830461
Minimum0
Maximum34
Zeros2169
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2025-07-06T23:08:43.177358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile7
Maximum34
Range34
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0279866
Coefficient of variation (CV)0.72869315
Kurtosis4.0653916
Mean2.7830461
Median Absolute Deviation (MAD)1
Skewness1.6504155
Sum912597
Variance4.1127298
MonotonicityNot monotonic
2025-07-06T23:08:43.424157image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 104400
30.6%
2 78850
23.1%
3 52705
15.4%
4 34194
 
10.0%
5 22310
 
6.5%
6 13871
 
4.1%
7 8367
 
2.4%
8 4926
 
1.4%
9 2753
 
0.8%
0 2169
 
0.6%
Other values (17) 3368
 
1.0%
(Missing) 13793
 
4.0%
ValueCountFrequency (%)
0 2169
 
0.6%
1 104400
30.6%
2 78850
23.1%
3 52705
15.4%
4 34194
 
10.0%
5 22310
 
6.5%
6 13871
 
4.1%
7 8367
 
2.4%
8 4926
 
1.4%
9 2753
 
0.8%
ValueCountFrequency (%)
34 1
 
< 0.1%
28 1
 
< 0.1%
25 2
 
< 0.1%
23 1
 
< 0.1%
22 6
 
< 0.1%
21 3
 
< 0.1%
20 7
 
< 0.1%
19 10
< 0.1%
18 15
< 0.1%
17 19
< 0.1%

RATIO_MORA_DEL_SALDO_TOTAL_REPORTADO
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct144278
Distinct (%)43.3%
Missing8489
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean0.8786993
Minimum-0.077785564
Maximum3.2288855
Zeros3979
Zeros (%)1.2%
Negative20
Negative (%)< 0.1%
Memory size5.2 MiB
2025-07-06T23:08:43.685483image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-0.077785564
5-th percentile0.23379529
Q10.90775458
median1
Q31
95-th percentile1
Maximum3.2288855
Range3.306671
Interquartile range (IQR)0.09224542

Descriptive statistics

Standard deviation0.24419099
Coefficient of variation (CV)0.27790051
Kurtosis3.8709654
Mean0.8786993
Median Absolute Deviation (MAD)0
Skewness-2.1985251
Sum292797.54
Variance0.059629237
MonotonicityNot monotonic
2025-07-06T23:08:43.940259image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 183342
53.7%
0 3979
 
1.2%
0.9411764706 10
 
< 0.1%
0.5 9
 
< 0.1%
0.8 9
 
< 0.1%
0.6666666667 8
 
< 0.1%
0.972972973 7
 
< 0.1%
0.9565217391 7
 
< 0.1%
0.875 7
 
< 0.1%
0.9756097561 6
 
< 0.1%
Other values (144268) 145833
42.7%
(Missing) 8489
 
2.5%
ValueCountFrequency (%)
-0.07778556412 1
< 0.1%
-0.07655172414 1
< 0.1%
-0.07607950651 1
< 0.1%
-0.04926764314 1
< 0.1%
-0.03693843594 1
< 0.1%
-0.01614545455 1
< 0.1%
-0.01143858203 1
< 0.1%
-0.01091874877 1
< 0.1%
-0.01041471195 1
< 0.1%
-0.007597535934 1
< 0.1%
ValueCountFrequency (%)
3.228885461 1
< 0.1%
3.000393109 1
< 0.1%
1.968020595 1
< 0.1%
1.437936554 1
< 0.1%
1.428925108 1
< 0.1%
1.413683016 1
< 0.1%
1.402985075 1
< 0.1%
1.237114907 1
< 0.1%
1.236824158 1
< 0.1%
1.227737779 1
< 0.1%

RANGO_EDAD_1
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)< 0.1%
Missing3722
Missing (%)1.1%
Memory size22.7 MiB
36-45
107608 
29-35
81646 
46-55
72221 
56-65
35147 
22-28
32616 
Other values (3)
 
8746

Length

Max length5
Median length5
Mean length4.9544594
Min length3

Characters and Unicode

Total characters1674528
Distinct characters11
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row36-45
2nd row36-45
3rd row56-65
4th row66+
5th row56-65

Common Values

ValueCountFrequency (%)
36-45 107608
31.5%
29-35 81646
23.9%
46-55 72221
21.1%
56-65 35147
 
10.3%
22-28 32616
 
9.5%
66+ 7696
 
2.3%
18-21 1046
 
0.3%
66+ 4
 
< 0.1%
(Missing) 3722
 
1.1%

Length

2025-07-06T23:08:44.197461image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T23:08:44.428892image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
36-45 107608
31.8%
29-35 81646
24.2%
46-55 72221
21.4%
56-65 35147
 
10.4%
22-28 32616
 
9.7%
66 7700
 
2.3%
18-21 1046
 
0.3%

Most occurring characters

ValueCountFrequency (%)
5 403990
24.1%
- 330284
19.7%
6 265523
15.9%
3 189254
11.3%
2 180540
10.8%
4 179829
10.7%
9 81646
 
4.9%
8 33662
 
2.0%
+ 7700
 
0.5%
1 2092
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1336536
79.8%
Dash Punctuation 330284
 
19.7%
Math Symbol 7700
 
0.5%
Space Separator 8
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 403990
30.2%
6 265523
19.9%
3 189254
14.2%
2 180540
13.5%
4 179829
13.5%
9 81646
 
6.1%
8 33662
 
2.5%
1 2092
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 330284
100.0%
Math Symbol
ValueCountFrequency (%)
+ 7700
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1674528
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 403990
24.1%
- 330284
19.7%
6 265523
15.9%
3 189254
11.3%
2 180540
10.8%
4 179829
10.7%
9 81646
 
4.9%
8 33662
 
2.0%
+ 7700
 
0.5%
1 2092
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1674528
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 403990
24.1%
- 330284
19.7%
6 265523
15.9%
3 189254
11.3%
2 180540
10.8%
4 179829
10.7%
9 81646
 
4.9%
8 33662
 
2.0%
+ 7700
 
0.5%
1 2092
 
0.1%
Distinct86
Distinct (%)< 0.1%
Missing2664
Missing (%)0.8%
Memory size5.2 MiB
Minimum2018-10-23 00:00:00+00:00
Maximum2022-07-22 00:00:00+00:00
2025-07-06T23:08:44.676122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:44.981725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PUNTAJE_1
Unsupported

REJECTED  UNSUPPORTED 

Missing3015
Missing (%)0.9%
Memory size24.2 MiB

INGRESOS_ESTIMADOS_DATACREDITO_1
Unsupported

REJECTED  UNSUPPORTED 

Missing3022
Missing (%)0.9%
Memory size24.7 MiB

CUOTAS_MERCADO_1
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct65232
Distinct (%)19.3%
Missing3538
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean17092.774
Minimum0
Maximum3265199
Zeros23662
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2025-07-06T23:08:45.232348image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11351
median4873
Q314857.25
95-th percentile76291.55
Maximum3265199
Range3265199
Interquartile range (IQR)13506.25

Descriptive statistics

Standard deviation40021.803
Coefficient of variation (CV)2.3414457
Kurtosis224.57521
Mean17092.774
Median Absolute Deviation (MAD)4408
Skewness8.6157114
Sum5.7802292 × 109
Variance1.6017447 × 109
MonotonicityNot monotonic
2025-07-06T23:08:45.488589image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23662
 
6.9%
61 259
 
0.1%
34 214
 
0.1%
75 193
 
0.1%
99 180
 
0.1%
27 171
 
0.1%
30 169
 
< 0.1%
113 169
 
< 0.1%
40 157
 
< 0.1%
100 156
 
< 0.1%
Other values (65222) 312838
91.6%
(Missing) 3538
 
1.0%
ValueCountFrequency (%)
0 23662
6.9%
1 42
 
< 0.1%
2 21
 
< 0.1%
3 17
 
< 0.1%
4 10
 
< 0.1%
5 14
 
< 0.1%
6 7
 
< 0.1%
7 13
 
< 0.1%
8 20
 
< 0.1%
9 16
 
< 0.1%
ValueCountFrequency (%)
3265199 1
< 0.1%
1538662 1
< 0.1%
1449466 1
< 0.1%
1391937 1
< 0.1%
1376518 1
< 0.1%
1314853 1
< 0.1%
1252058 1
< 0.1%
1181279 1
< 0.1%
1146368 1
< 0.1%
1106267 1
< 0.1%

SALDO_TOTAL_DATACREDITO_1
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct81784
Distinct (%)25.1%
Missing15231
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean26125.492
Minimum0
Maximum4070959
Zeros6494
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2025-07-06T23:08:45.968183image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile311
Q12819
median8670
Q326282
95-th percentile108975.5
Maximum4070959
Range4070959
Interquartile range (IQR)23463

Descriptive statistics

Standard deviation56003.243
Coefficient of variation (CV)2.1436245
Kurtosis431.41282
Mean26125.492
Median Absolute Deviation (MAD)7216
Skewness11.927985
Sum8.5293199 × 109
Variance3.1363632 × 109
MonotonicityNot monotonic
2025-07-06T23:08:46.232973image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6494
 
1.9%
44 94
 
< 0.1%
55 91
 
< 0.1%
29 90
 
< 0.1%
35 84
 
< 0.1%
49 76
 
< 0.1%
39 74
 
< 0.1%
200 72
 
< 0.1%
61 71
 
< 0.1%
100 68
 
< 0.1%
Other values (81774) 319261
93.4%
(Missing) 15231
 
4.5%
ValueCountFrequency (%)
0 6494
1.9%
1 16
 
< 0.1%
2 5
 
< 0.1%
3 9
 
< 0.1%
4 6
 
< 0.1%
5 13
 
< 0.1%
6 1
 
< 0.1%
7 4
 
< 0.1%
8 3
 
< 0.1%
9 11
 
< 0.1%
ValueCountFrequency (%)
4070959 1
< 0.1%
3785590 1
< 0.1%
3729985 1
< 0.1%
3517847 1
< 0.1%
3257853 1
< 0.1%
2967597 1
< 0.1%
2589781 1
< 0.1%
2563785 1
< 0.1%
2188463 1
< 0.1%
1966112 1
< 0.1%

SALDO_CASTIGO_DATACREDITO_1
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct72316
Distinct (%)25.1%
Missing53215
Missing (%)15.6%
Infinite0
Infinite (%)0.0%
Mean23672.54
Minimum0
Maximum3803208
Zeros1059
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2025-07-06T23:08:46.517297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile578
Q12549
median7669
Q322463
95-th percentile101972.5
Maximum3803208
Range3803208
Interquartile range (IQR)19914

Descriptive statistics

Standard deviation52219.779
Coefficient of variation (CV)2.2059221
Kurtosis524.40045
Mean23672.54
Median Absolute Deviation (MAD)6249
Skewness12.840182
Sum6.8293148 × 109
Variance2.7269053 × 109
MonotonicityNot monotonic
2025-07-06T23:08:46.797526image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1059
 
0.3%
100 119
 
< 0.1%
753 117
 
< 0.1%
700 102
 
< 0.1%
55 100
 
< 0.1%
600 94
 
< 0.1%
500 91
 
< 0.1%
1000 83
 
< 0.1%
800 78
 
< 0.1%
999 64
 
< 0.1%
Other values (72306) 286584
83.9%
(Missing) 53215
 
15.6%
ValueCountFrequency (%)
0 1059
0.3%
6 2
 
< 0.1%
7 2
 
< 0.1%
9 3
 
< 0.1%
10 6
 
< 0.1%
11 3
 
< 0.1%
12 6
 
< 0.1%
13 7
 
< 0.1%
14 7
 
< 0.1%
15 11
 
< 0.1%
ValueCountFrequency (%)
3803208 1
< 0.1%
3785590 1
< 0.1%
3705766 1
< 0.1%
3514514 1
< 0.1%
3257853 1
< 0.1%
2557043 1
< 0.1%
2479062 1
< 0.1%
2187470 1
< 0.1%
1966112 1
< 0.1%
1649723 1
< 0.1%

BANCOS_CREDITOS_ACTIVOS_1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)< 0.1%
Missing3053
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean3.5104369
Minimum0
Maximum29
Zeros12318
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2025-07-06T23:08:47.019198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile7
Maximum29
Range29
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0711341
Coefficient of variation (CV)0.58999324
Kurtosis1.6471729
Mean3.5104369
Median Absolute Deviation (MAD)1
Skewness0.90174942
Sum1188820
Variance4.2895963
MonotonicityNot monotonic
2025-07-06T23:08:47.251675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3 70526
20.6%
2 65720
19.2%
4 58205
17.0%
5 40194
11.8%
1 39003
11.4%
6 24601
 
7.2%
7 13732
 
4.0%
0 12318
 
3.6%
8 7110
 
2.1%
9 3684
 
1.1%
Other values (15) 3560
 
1.0%
(Missing) 3053
 
0.9%
ValueCountFrequency (%)
0 12318
 
3.6%
1 39003
11.4%
2 65720
19.2%
3 70526
20.6%
4 58205
17.0%
5 40194
11.8%
6 24601
 
7.2%
7 13732
 
4.0%
8 7110
 
2.1%
9 3684
 
1.1%
ValueCountFrequency (%)
29 1
 
< 0.1%
25 1
 
< 0.1%
23 1
 
< 0.1%
21 2
 
< 0.1%
20 4
 
< 0.1%
19 13
 
< 0.1%
18 11
 
< 0.1%
17 18
 
< 0.1%
16 17
 
< 0.1%
15 62
< 0.1%

RATIO_MORA_DEL_SALDO_TOTAL_REPORTADO_1
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct109391
Distinct (%)35.6%
Missing34599
Missing (%)10.1%
Infinite0
Infinite (%)0.0%
Mean0.88424506
Minimum0
Maximum1
Zeros6720
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2025-07-06T23:08:47.513387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1509545
Q10.95548086
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.044519136

Descriptive statistics

Standard deviation0.25733447
Coefficient of variation (CV)0.29102166
Kurtosis4.4184658
Mean0.88424506
Median Absolute Deviation (MAD)0
Skewness-2.3723225
Sum271557.85
Variance0.066221028
MonotonicityNot monotonic
2025-07-06T23:08:47.769554image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 189433
55.4%
0 6720
 
2.0%
0.5 12
 
< 0.1%
0.8571428571 9
 
< 0.1%
0.9090909091 8
 
< 0.1%
0.9615384615 8
 
< 0.1%
0.9230769231 7
 
< 0.1%
0.9375 7
 
< 0.1%
0.75 6
 
< 0.1%
0.8965517241 6
 
< 0.1%
Other values (109381) 110891
32.5%
(Missing) 34599
 
10.1%
ValueCountFrequency (%)
0 6720
2.0%
6.997243086 × 10-51
 
< 0.1%
0.0001500180022 1
 
< 0.1%
0.0001638538424 1
 
< 0.1%
0.0002108082637 1
 
< 0.1%
0.0002341621253 1
 
< 0.1%
0.0002486130941 1
 
< 0.1%
0.0002684464357 1
 
< 0.1%
0.0002887302797 1
 
< 0.1%
0.000295476523 1
 
< 0.1%
ValueCountFrequency (%)
1 189433
55.4%
0.9999986363 1
 
< 0.1%
0.9999984087 1
 
< 0.1%
0.99999743 1
 
< 0.1%
0.9999965015 1
 
< 0.1%
0.9999962353 1
 
< 0.1%
0.9999960386 1
 
< 0.1%
0.9999955013 1
 
< 0.1%
0.9999947652 1
 
< 0.1%
0.999994756 1
 
< 0.1%

Tipo_Cliente
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.5 MiB
0
194993 
1
146713 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters341706
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 194993
57.1%
1 146713
42.9%

Length

2025-07-06T23:08:47.991535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T23:08:48.182158image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 194993
57.1%
1 146713
42.9%

Most occurring characters

ValueCountFrequency (%)
0 194993
57.1%
1 146713
42.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 341706
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 194993
57.1%
1 146713
42.9%

Most occurring scripts

ValueCountFrequency (%)
Common 341706
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 194993
57.1%
1 146713
42.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 341706
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 194993
57.1%
1 146713
42.9%

Base
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.1 MiB
Aplicación
194993 
Desarrollo
146713 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3417060
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDesarrollo
2nd rowAplicación
3rd rowAplicación
4th rowAplicación
5th rowAplicación

Common Values

ValueCountFrequency (%)
Aplicación 194993
57.1%
Desarrollo 146713
42.9%

Length

2025-07-06T23:08:48.426674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T23:08:48.633533image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
aplicación 194993
57.1%
desarrollo 146713
42.9%

Most occurring characters

ValueCountFrequency (%)
l 488419
14.3%
i 389986
11.4%
c 389986
11.4%
a 341706
10.0%
r 293426
8.6%
o 293426
8.6%
A 194993
 
5.7%
p 194993
 
5.7%
ó 194993
 
5.7%
n 194993
 
5.7%
Other values (3) 440139
12.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3075354
90.0%
Uppercase Letter 341706
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 488419
15.9%
i 389986
12.7%
c 389986
12.7%
a 341706
11.1%
r 293426
9.5%
o 293426
9.5%
p 194993
 
6.3%
ó 194993
 
6.3%
n 194993
 
6.3%
e 146713
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
A 194993
57.1%
D 146713
42.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 3417060
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 488419
14.3%
i 389986
11.4%
c 389986
11.4%
a 341706
10.0%
r 293426
8.6%
o 293426
8.6%
A 194993
 
5.7%
p 194993
 
5.7%
ó 194993
 
5.7%
n 194993
 
5.7%
Other values (3) 440139
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3222067
94.3%
None 194993
 
5.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 488419
15.2%
i 389986
12.1%
c 389986
12.1%
a 341706
10.6%
r 293426
9.1%
o 293426
9.1%
A 194993
 
6.1%
p 194993
 
6.1%
n 194993
 
6.1%
D 146713
 
4.6%
Other values (2) 293426
9.1%
None
ValueCountFrequency (%)
ó 194993
100.0%

Interactions

2025-07-06T23:08:25.488076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:47.694635image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:51.307241image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:54.797427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:57.961791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:01.221445image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:04.655262image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:07.827941image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:11.308246image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:14.531895image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:18.238768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:21.286443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:25.804296image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:48.090792image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:51.571012image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:55.071966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:58.207128image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:01.510199image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:04.925217image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:08.086507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:11.587080image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:14.841175image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:18.509972image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:21.547907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:26.088466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:48.432305image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:51.839562image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:55.321903image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:58.456434image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:01.779623image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:05.212767image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:08.350159image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:11.837481image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:15.139557image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:18.818985image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:21.803044image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:26.458308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:48.759033image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:52.162426image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:55.556871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:58.738850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:02.053222image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:05.472662image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:08.622146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:12.115091image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:15.469132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:19.076375image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:22.053026image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:26.749727image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:49.054974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:52.456347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:55.809503image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:59.024070image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:02.360899image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:05.747662image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:08.870749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:12.386342image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:15.757456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:19.320149image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:22.304827image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:27.065763image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:49.344990image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:52.785856image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:56.056622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:59.305381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:02.625582image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:06.011100image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:09.141308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:12.654344image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:16.114130image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:19.580338image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:22.647252image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:27.384367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:49.672236image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:53.077722image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:56.340268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:59.585986image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:02.921015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:06.284620image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:09.405495image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:12.888180image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:16.439327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:19.821946image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:23.228290image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:27.693578image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:49.938628image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:53.338044image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:56.592124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:59.861863image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:03.207879image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:06.566200image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:09.991036image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:13.138991image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:16.789341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:20.054144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:23.616160image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:27.991828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:50.229701image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:53.589921image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:56.855988image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:00.123184image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:03.517624image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:06.820704image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:10.256274image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:13.387547image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:17.097367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:20.303957image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:23.928372image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:28.287367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:50.498806image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:53.856600image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:57.165373image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:00.399120image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:03.801027image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:07.055448image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:10.522917image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:13.649744image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:17.466371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:20.536749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:24.529003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:28.594639image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:50.786549image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:54.193659image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:57.442386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:00.674301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:04.091660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:07.322732image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:10.798746image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:13.958094image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:17.736230image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:20.787367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:24.856740image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:28.878041image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:51.050306image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:54.457013image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:07:57.694488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:00.942343image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:04.375207image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:07.554752image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:11.067369image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:14.241596image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:17.997338image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:21.036708image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-07-06T23:08:25.146432image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2025-07-06T23:08:48.854604image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
BANCOS_CREDITOS_ACTIVOSBANCOS_CREDITOS_ACTIVOS_1BaseCUOTAS_MERCADO_1Edad__cEntidad_principalGenero__cMotivo_Renuencia_ClienteOperado_Por__cRANGO_EDADRANGO_EDAD_1RATIO_MORA_DEL_SALDO_TOTAL_REPORTADORATIO_MORA_DEL_SALDO_TOTAL_REPORTADO_1SALDO_CASTIGO_DATACREDITOSALDO_CASTIGO_DATACREDITO_1SALDO_TOTAL_DATACREDITOSALDO_TOTAL_DATACREDITO_1Saldo_Capital_clienteSaldo_Total_clienteTipo_Clienteestadolocalizado_historico
BANCOS_CREDITOS_ACTIVOS1.0000.5480.0090.3000.0880.1610.0180.0160.0700.0360.027-0.368-0.2470.2270.2100.3700.2920.1030.0310.0090.0140.031
BANCOS_CREDITOS_ACTIVOS_10.5481.0000.0290.4640.0980.0910.0220.0400.0580.0380.051-0.300-0.2960.3010.3050.4090.4580.1530.1350.0290.0400.094
Base0.0090.0291.000-0.129-0.0430.3670.0350.9660.6720.1390.118-0.160-0.204-0.017-0.0550.016-0.0830.091-0.0271.0000.6390.832
CUOTAS_MERCADO_10.3000.464-0.1291.0000.2610.0420.0080.0150.0620.0160.017-0.0460.0650.6450.8370.6380.7980.5470.5930.0200.0130.012
Edad__c0.0880.098-0.0430.2611.0000.2000.0490.0700.1090.5810.562-0.067-0.0250.2550.1870.2540.2090.2040.2480.1270.0920.070
Entidad_principal0.1610.0910.3670.0420.2001.0000.1160.2070.4180.1840.193-0.133-0.117-0.0350.0960.0400.100-0.044-0.0930.3670.2190.235
Genero__c0.0180.0220.0350.0080.0490.1161.0000.0440.0880.0380.0560.0250.0280.0650.0520.0590.0490.0660.0610.0350.0370.020
Motivo_Renuencia_Cliente0.0160.0400.9660.0150.0700.2070.0441.0000.5270.0790.0730.1850.2360.0710.1340.0290.159-0.0410.0970.9660.6060.875
Operado_Por__c0.0700.0580.6720.0620.1090.4180.0880.5271.0000.1210.128-0.063-0.044-0.047-0.103-0.039-0.0850.0470.0120.6720.5830.494
RANGO_EDAD0.0360.0380.1390.0160.5810.1840.0380.0790.1211.0000.703-0.035-0.0050.2710.2430.2630.2590.2090.2700.1390.0950.081
RANGO_EDAD_10.0270.0510.1180.0170.5620.1930.0560.0730.1280.7031.000-0.0400.0030.3000.2570.2900.2700.2460.3050.1180.0840.070
RATIO_MORA_DEL_SALDO_TOTAL_REPORTADO-0.368-0.300-0.160-0.046-0.067-0.1330.0250.185-0.063-0.035-0.0401.0000.5260.047-0.016-0.198-0.144-0.0580.0170.1250.0800.077
RATIO_MORA_DEL_SALDO_TOTAL_REPORTADO_1-0.247-0.296-0.2040.065-0.025-0.1170.0280.236-0.044-0.0050.0030.5261.0000.0300.053-0.121-0.064-0.0030.0660.1850.1660.103
SALDO_CASTIGO_DATACREDITO0.2270.301-0.0170.6450.255-0.0350.0650.071-0.0470.2710.3000.0470.0301.0000.8080.8810.7090.7330.7420.0130.0100.007
SALDO_CASTIGO_DATACREDITO_10.2100.305-0.0550.8370.1870.0960.0520.134-0.1030.2430.257-0.0160.0530.8081.0000.7710.9150.6980.7250.0150.0110.012
SALDO_TOTAL_DATACREDITO0.3700.4090.0160.6380.2540.0400.0590.029-0.0390.2630.290-0.198-0.1210.8810.7711.0000.8070.6900.6750.0130.0090.007
SALDO_TOTAL_DATACREDITO_10.2920.458-0.0830.7980.2090.1000.0490.159-0.0850.2590.270-0.144-0.0640.7090.9150.8071.0000.6090.6310.0170.0130.009
Saldo_Capital_cliente0.1030.1530.0910.5470.204-0.0440.066-0.0410.0470.2090.246-0.058-0.0030.7330.6980.6900.6091.0000.9390.0130.0190.003
Saldo_Total_cliente0.0310.135-0.0270.5930.248-0.0930.0610.0970.0120.2700.3050.0170.0660.7420.7250.6750.6310.9391.0000.0100.0100.006
Tipo_Cliente0.0090.0291.0000.0200.1270.3670.0350.9660.6720.1390.1180.1250.1850.0130.0150.0130.0170.0130.0101.0000.6390.832
estado0.0140.0400.6390.0130.0920.2190.0370.6060.5830.0950.0840.0800.1660.0100.0110.0090.0130.0190.0100.6391.0000.412
localizado_historico0.0310.0940.8320.0120.0700.2350.0200.8750.4940.0810.0700.0770.1030.0070.0120.0070.0090.0030.0060.8320.4121.000

Missing values

2025-07-06T23:08:29.525036image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-06T23:08:30.822250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-06T23:08:33.033747image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Contacto__cEntidad_principalOperado_Por__cSaldo_Capital_clienteSaldo_Total_clienteEdad__cGenero__cCiudadestadolocalizado_historicoMotivo_Renuencia_ClienteRANGO_EDADFECHA_RECEPCIONPUNTAJEINGRESOS_ESTIMADOS_DATACREDITOSALDO_TOTAL_DATACREDITOSALDO_CASTIGO_DATACREDITOBANCOS_CREDITOS_ACTIVOSRATIO_MORA_DEL_SALDO_TOTAL_REPORTADORANGO_EDAD_1FECHA_RECEPCION_1PUNTAJE_1INGRESOS_ESTIMADOS_DATACREDITO_1CUOTAS_MERCADO_1SALDO_TOTAL_DATACREDITO_1SALDO_CASTIGO_DATACREDITO_1BANCOS_CREDITOS_ACTIVOS_1RATIO_MORA_DEL_SALDO_TOTAL_REPORTADO_1Tipo_ClienteBase
00036e000043bxo7AAABANCO DE BOGOTA4AYS_BB44873605710634145434.0MSIN INFORMACIONPOR_ACORDARLOCALIZADOCLIENTE NO PERDIDO QUIEN NO ACEPTA POR DIVERSOS MOTIVOS29-352019-03-09 00:00:00 UTC389 - 8691689-175495424.055851.02.00.58529336-452022-03-07 00:00:00 UTC366 - 4371521 - 1860752.0124652.098144.04.00.7873441Desarrollo
10035A00003XlKblQAFGRUPO SOLDIGITAL26982422738123122.0MSIN INFORMACIONNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0Aplicación
20036e000046v5rCAAQGRUPO SOLDIGITAL20614920919641.0MNaNNaNNaNNaN36-452022-05-03 00:00:00 UTCEXCLUSIONES1521 - 186094.0NaN1.01.00000036-452022-05-03 00:00:00 UTCEXCLUSIONES1521 - 18600.094.0NaN1.01.0000000Aplicación
30035A00003XihQkQAJGRUPO SOLDIGITAL2701257274118360.0MSIN INFORMACIONNaNNaNNaN56-652019-03-21 00:00:00 UTC248 - 2692426-294710797.01750.01.00.95989656-652022-05-03 00:00:00 UTC222 - 2933421 - 38608807.09977.0723.04.00.9358520Aplicación
40035A00003XihVTQAZGRUPO SOLDIGITAL2046427207667473.0MSIN INFORMACIONNaNNaNNaN66+2019-03-21 00:00:00 UTC331 - 3521755-1911580.0358.01.00.71724166+2022-05-03 00:00:00 UTC294 - 3651521 - 1860529.0457.0358.03.01.0000000Aplicación
50036e000046v5raAAAGRUPO SOLDIGITAL83850385089761.0MNaNNaNNaNNaN56-652019-03-21 00:00:00 UTC389 - 8691324-1514324.00.00.01.00000056-652022-05-03 00:00:00 UTC726 - 7971521 - 18600.0NaNNaN0.0NaN0Aplicación
60035A00003XlKatQAFGRUPO SOLDIGITAL628330637617122.0MSIN INFORMACIONNaNNaNCLIENTE PERDIDO56-652022-05-03 00:00:00 UTCEXCLUSIONES2221 - 26001430.01028.02.01.00000056-652022-05-03 00:00:00 UTCEXCLUSIONES2221 - 26001028.01430.01028.02.01.0000000Aplicación
70035A00003XihSVQAZGRUPO SOLDIGITAL1669268169394139.0MSIN INFORMACIONNaNNaNNaN36-452019-03-21 00:00:00 UTC389 - 8691324-15149697.00.00.00.00000036-452022-05-03 00:00:00 UTCEXCLUSIONES1201 - 1520109.09648.09648.01.01.0000000Aplicación
80035A00003XihVBQAZGRUPO SOLDIGITAL3363934341365474.0MSIN INFORMACIONNaNNaNNaN66+2019-03-21 00:00:00 UTC150 - 2392948-1854859619.033087.03.01.00000066+2022-05-03 00:00:00 UTCEXCLUSIONES1521 - 18600.0543.0NaN1.01.0000000Aplicación
90035A00003XihV6QAJGRUPO SOLDIGITAL987561100215857.0MSIN INFORMACIONNaNNaNNaN46-552019-03-21 00:00:00 UTCEXCLUSIONES1912-22101450.01450.01.01.00000046-552022-05-03 00:00:00 UTC222 - 2933001 - 3420560.0585.0161.03.00.3179490Aplicación
Contacto__cEntidad_principalOperado_Por__cSaldo_Capital_clienteSaldo_Total_clienteEdad__cGenero__cCiudadestadolocalizado_historicoMotivo_Renuencia_ClienteRANGO_EDADFECHA_RECEPCIONPUNTAJEINGRESOS_ESTIMADOS_DATACREDITOSALDO_TOTAL_DATACREDITOSALDO_CASTIGO_DATACREDITOBANCOS_CREDITOS_ACTIVOSRATIO_MORA_DEL_SALDO_TOTAL_REPORTADORANGO_EDAD_1FECHA_RECEPCION_1PUNTAJE_1INGRESOS_ESTIMADOS_DATACREDITO_1CUOTAS_MERCADO_1SALDO_TOTAL_DATACREDITO_1SALDO_CASTIGO_DATACREDITO_1BANCOS_CREDITOS_ACTIVOS_1RATIO_MORA_DEL_SALDO_TOTAL_REPORTADO_1Tipo_ClienteBase
3416970035A00003XiSJZQA3BANCO DE BOGOTA3MANTENIMIENTO-ESP-QNT67017491821218737.0MCALIPOR_CONTACTARNO_LOCALIZADOCLIENTE SIN NINGUN CONTACTO HISTORICO29-352018-11-18 00:00:00 UTC1501382798.3450338.017738.0NaN0.99860929-352022-03-07 00:00:00 UTC150 - 2213421 - 386025179.052953.050655.08.01.0000000Aplicación
3416980035A00003XiRbwQAFBANCO DE BOGOTA3MANTENIMIENTO-ESP-QNT75654901895321764.0MBOGOTA D.C.POR_CONTACTARNO_LOCALIZADOCLIENTE SIN NINGUN CONTACTO HISTORICO56-652018-11-18 00:00:00 UTC2462765596.68NaNNaNNaNNaN56-652022-03-07 00:00:00 UTC222 - 2933861 - 432020219.019951.019651.06.01.0000000Aplicación
3416990035A00003XiRwjQAFBANCO DE BOGOTA3MANTENIMIENTO-ESP-QNT72168441866611244.0FNEIVAPOR_CONTACTARNO_LOCALIZADOCLIENTE SIN NINGUN CONTACTO HISTORICO36-452018-11-18 00:00:00 UTC11328111.414489.014489.0NaN1.00000036-452022-03-07 00:00:00 UTC294 - 3653421 - 386023864.023749.023311.07.00.9911150Aplicación
3417000035A00003XiRnpQAFBANCO DE BOGOTA3MANTENIMIENTO-ESP-QNT89488033381525544.0FCALIPOR_CONTACTARNO_LOCALIZADOCLIENTE SIN NINGUN CONTACTO HISTORICO36-452018-11-18 00:00:00 UTC2112187477.62682.02154.0NaN1.00000046-552022-03-07 00:00:00 UTCEXCLUSIONES3861 - 432032463.032632.032632.04.01.0000000Aplicación
3417010035A00003XiRYTQA3BANCO DE BOGOTA3MANTENIMIENTO-ESP-QNT44002491109560264.0FBARANOAPOR_CONTACTARNO_LOCALIZADOCLIENTE SIN NINGUN CONTACTO HISTORICO56-652018-11-18 00:00:00 UTC2292945282.34NaNNaNNaNNaN56-652022-03-07 00:00:00 UTC222 - 2933421 - 386020832.021152.021152.07.01.0000000Aplicación
3417020035A00003XiRcWQAVBANCO DE BOGOTA3MANTENIMIENTO-ESP-QNT100557942313745264.0MCALIPOR_CONTACTARNO_LOCALIZADOCLIENTE SIN NINGUN CONTACTO HISTORICO56-652018-11-18 00:00:00 UTC2692351538.42NaNNaNNaNNaN56-652022-03-07 00:00:00 UTC294 - 3652601 - 300030985.028791.025080.04.01.0000000Aplicación
3417030035A00003XiRRsQANBANCO DE BOGOTA3MANTENIMIENTO-ESP-QNT74568322922917164.0MCALIPOR_CONTACTARNO_LOCALIZADOCLIENTE SIN NINGUN CONTACTO HISTORICO56-652018-11-18 00:00:00 UTC2162296851.4828115.017249.0NaN0.61810466+2022-03-07 00:00:00 UTC150 - 2213001 - 342026963.049838.030356.08.00.6162970Aplicación
3417040035A00003XiRsaQAFBANCO DE BOGOTA3MANTENIMIENTO-ESP-QNT94394052199758464.0MMANIZALESPOR_CONTACTARNO_LOCALIZADOCLIENTE SIN NINGUN CONTACTO HISTORICO56-652018-11-18 00:00:00 UTC1504937449.4464671.034227.0NaN0.54243866+2022-03-07 00:00:00 UTC150 - 2213861 - 432033010.074661.040910.010.00.5593680Aplicación
3417050035A00003XiS58QAFBANCO DE BOGOTA3MANTENIMIENTO-ESP-QNT60542861561790053.0MCALIPOR_CONTACTARNO_LOCALIZADOCLIENTE SIN NINGUN CONTACTO HISTORICO46-552018-11-18 00:00:00 UTC1943695274.6624065.022839.0NaN0.96505346-552022-03-07 00:00:00 UTC222 - 2932221 - 260016025.017882.016025.05.00.9087910Aplicación
3417060035A00003XiRl4QAFGIROS Y FINANZASMANTENIMIENTO-ESP-QNT149896103104700664.0MCALIPOR_CONTACTARNO_LOCALIZADOCLIENTE PERDIDO56-652018-11-18 00:00:00 UTC3853109343.16NaNNaNNaNNaN56-652022-03-07 00:00:00 UTC366 - 4375821 - 636067085.045425.045356.04.00.9984810Aplicación